• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.103-110
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• 2018

Studies on artificial intelligence have been developed for the past couple of decades. After a few periods of prosperity and recession, a new machine learning method, so-called Deep Learning, has been introduced. This is the result of high-quality big- data, an increase in computing power, and the development of new algorithms. The main targets for deep learning are 1D audio and 2D images. The application domain is being extended from a discriminative model, such as classification/segmentation, to a generative model. Currently, deep learning is used for processing 3D data. However, unlike 2D, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become more popular owing to advances in 3D vision technology, the generation/acquisition of 3D data remains a very difficult problem. Moreover, it is not easy to directly apply an existing network model, such as a convolution network, owing to the variety of 3D data representations. In this paper, we summarize the 3D deep learning technology that have started to be developed within the last 2 years.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2724-2731
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• 2014

As the future national defense plan of government focus on advanced weapon system, military maintenance facility becomes more important. However, military maintenance facility has been managed by director's experience and simple mathematical calculation until now. Thus, the optimization for the management of military maintenance facility is suggested by more scientistic and logical methods in this study. The study follows the procedure below. First, simulation is designed according to the analysis of military maintenance facility. Second, independent variable and dependent variable are defined for optimization. Independent Variable includes the number of maintenance machine, transportation machine, worker in the details of military maintenance facility operation, and dependent variable involves total maintenance time affected by independent variable. Third, warmup analysis is performed to get warmup period, based on the simulation model. Fourth, the optimal combination is computed with evolution strategy, meta-heuristic, to enhance military maintenance management. By the optimal combination, the management of military maintenance facility can gain the biggest effect against the limited cost. In the future, the multipurpose study, to analyze the military maintenance facility covering various weapon system equipments, will be performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.111-120
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• 2021

A tractor is an agricultural machine that performs farm work, such as cultivation, soil preparation, loading, bailing, and transporting, through attached working implements. Farm work must be carried out on time per the growing season of crops. As a result, the reliability of a tractor's transmission is vital. Ideally, the transmission's design should reflect the actual load during agricultural work; however, configuring such a measurement system is time- and cost-intensive. The design and analysis of a transmission are, therefore, mainly performed by empirical methods. In this study, a tractor with a measurement system was used to measure the actual working load in the field. Its hydro-mechanical transmission was then analyzed using the measured load. It was found that the velocity factor, load distribution factor, lubrication factor, roughness factor, relative notch sensitivity factor, and life factor affect the gear strength of the transmission. Also, loading conditions have a significant influence on the reliability of the transmission. It is believed that transmission reliability can be enhanced by analyzing the actual load on the transmission, as performed in this study.

• Transactions of Materials Processing
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• v.22 no.7
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• pp.401-406
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• 2013

The use of roll-formed products increases every year due to its advantages, such as high production rates, reduced tooling cost and improved quality. However, till now, it is limited to part profiles with constant cross section. In recent years, the flexible roll forming process, which allows variable cross sections of profiles by adaptive roll stands, was developed. In this study, an attempt to optimize profile design for the flexible roll forming process was performed. An equation that predicts the longitudinal strain for part geometries with variable cross-sections was proposed. The relationship between geometrical parameters and the longitudinal strain was analyzed and investigations on the optimal profile design were performed. Experiments were conducted with a lab-scale roll forming machine to validate the proposed equation. The results show that the profile design method proposed in this study is feasible and parts with variable cross sections can be successfully fabricated with the flexible roll forming process.

• East Asian Economic Review
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• v.28 no.3
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• pp.359-388
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• 2024

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry's health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study's findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Biosystems Engineering
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• v.33 no.2
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• pp.83-93
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• 2008

In order to save labor and cost, direct seeding has been considered as an important alternative to the machine transplanting in rice cultivation. Current direct seeding machines for rice in Korea drill irregularly under various operating conditions. This study was conducted to develope a precision seeder which enables the accurate, even-spaced in row placement of rice seeds at uniform depths of 3-4 cm on dry paddy. Design, construction and performance evaluation of the precision seeder were carried out. The tractor rear-mounted type 8-rows precision seeder which performs seeding in addition to fertilizing, ditching, and rotary tilling works on dry paddy was developed. Main components of the seeder were ditcher and leveller, rotary tiller, powered roller type furrow opener, seeding device, powered roller type furrow covering and firming device, hydraulic unit, seeding speed control system, power transmission system, hitch and frame. Ditching, furrow opening, and seed covering and firming performances were good and seeding depths of 2-4 cm could be maintained. Planting accuracies and planting precisions were within 13.6%, and 31.2%, respectively, for planting space of 15 cm, and seeding velocity of 0.5 m/s. These mean variations of average planting space were within 2.1 cm, and 90% of seeds in a hill were seeded within 4.7 cm of hill length, respectively. Error ratios between setting planting space and measured average planting space were shown within 6.7%. Therefore the seeder showed good planting performance up to seeding velocity of 0.5 m/s in field tests. And field capacity of the seeder was about 0.28 ha/hour.

• Journal of Biosystems Engineering
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• v.30 no.5 s.112
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• pp.261-267
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• 2005

In order to save labor and cost, direct seeding has been considered as an important alternative to the machine transplanting in rice cultivation. As current seeders for direct seeding of rice seeds drill irregular amount of seeds under various operating conditions, conventional drilling should be turned to precision planting which enables accurate placement of proper amount of rice seeds at equal intervals within rows. In this study, design, construction and performance evaluation of a precision seed metering device for planting of rice seeds were carried out. As prototype, the conventional roller type seed metering device was modified for planting: increasing diameter of metering roller, setting 2 or 4 seed cells on metering roller, adding seed discharging lid and its driving cam mechanism. Through performance tests for prototype and the current seed metering device, number of seeds in a hill, planting space and its error ratio, coefficient of variation of planting space (planting accuracy), and seeding length of $90\%$ of seeds in a hill divided by planting space (planting precision) at setting planting spaces of 15, and 20cm, seeding heights of 10, and 20cm, and seeding speeds of 0.1, 0.2, and 0.5m/s were investigated. Prototype showed better seed planting performance than the current seed metering devices. When setting planting space of 15 cm and seeding height of 10cm, prototype with 2 seed cells showed that variations of planting space and seeding lengths of $90\%$ of seeds in a hill at up to seeding speed of 0.5m/s were within 0.9cm, and 3.6cm, respectively.